{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats\n",
    "import random\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_ROOT = os.path.normpath(os.path.join(sys.path[0], '..'))\n",
    "DATA_DIR = os.path.join(PROJECT_ROOT, 'data')\n",
    "\n",
    "def list_absolute(directory):\n",
    "    return sorted([\n",
    "        os.path.join(directory, name)\n",
    "        for name in os.listdir(directory)\n",
    "    ])\n",
    "\n",
    "def load_data(kind):\n",
    "    data_dir = os.path.join(PROJECT_ROOT, 'data', 'processed', kind)\n",
    "    Xs = np.concatenate([\n",
    "        np.load(file) for file in list_absolute(os.path.join(data_dir, 'X'))\n",
    "    ])\n",
    "    Ys = np.concatenate([\n",
    "        np.load(file) for file in list_absolute(os.path.join(data_dir, 'Y'))\n",
    "    ])\n",
    "    \n",
    "    return (Xs, Ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(Xs_train, Ys_train) = load_data('train')\n",
    "(Xs_test, Ys_test) = load_data('test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(12, 8))\n",
    "sns.distplot(Ys_train, label='Train')\n",
    "sns.distplot(Ys_test, label='Test')\n",
    "plt.title(\"Distribution of times until next seizure\")\n",
    "plt.xlabel('Time until next seizure (s)')\n",
    "plt.ylabel('Probability density')\n",
    "plt.legend()\n",
    "plt.savefig('next-seizure-time-distribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
